Picture Graph: when real objects are removed and pictures were used to represent the objects and it uses, drawings or other pictoral representations of the objects under investigations. Statistics-consists of techniques and tools that aid in the collection, organization, summarization, and interpretation of a collection of information referred to as data; it tells a story (p.1, Dawn Cooper) Measurement- is a variable that takes on numeric values and could possibly use partial units. Ex. 5 1/2 years old, Ex: Height, weight Real Graph- A graph constructed using real objects to display the data. (p.14, Chelsey Jones) Categorical Variable- a variable that takes on categorical designations.Ex. eye color, major, age. (p. 13, Sarah Ross) Catergorical Graphs: Real, Pictograph, bar graph, circle graph, stacked bar graph( Bailey) Count- is a variable that takes on numerical variables and could not be answered in partial units. Ex. How many kids do you have? (Katie Minor) Frequency: How much of the data, but frequency is not part of the data (Bailey) Outcome: This helps you determine what variable you are dealing with (bailey) READ: No calculations, Read the information An example " What % of the 5th grade sampled score in the profieciency level in science in 1997?" (Bailey) DERIVE: Calculation, An example " How many of the 5th grade sample scored in the proficency level in science for 1999?" ( Bailey) INTERRET: Predictions, organize the information/ Read beyond the data, summarizing the data, An example " Is there any pattern exhitbited in the 5th grade sample proficincy level science pattern from 1997-1999?" (Bailey) DISTRIBUTION: How is the spread of the data (Bailey) SPREAD OUT: Data distributed unevenly at 1 end to the outher (Bailey) RANGE: Which tells us the difference between the lowest and highest points in our information, this tells us the SPREAD (Bailey) SPREAD: This is a description of how data is distributed which = the range ( variability) (Bailey) CLUMPS= clustering ( Bailey) HOLES= gaps ( bailey) BUMPS= mod ( Bailey) DISTRIBUTION= spread ( Bailey) SYMETRIC: the graph is equal on both sides, so if you were to cut the graph in half it would be the same on both sides (Bailey) Measures of center: (Bailey)
Mode: The outcome which occurs most often
Median: The outcome value in the middle, when in order from least to greatest.. this is our balancing point..( Bailey)
Mean: A fairshare of the outcomes, calculated by taking the sum of the outcomes divided by the number of outcomes (Jeannie) The mean can also be thought of as a balance point in a data set or as an "equalizer". (Dr B)
Outlier: a point on a box plot that is 1 1/2 box/interquartile range from the box. (KatieM) Deviation: how far values are from the mean. Standard Deviation: the square root of the total sum of all the distances from the data points to the mean. Mean Deviation: add absolute deviations together and get mean Variance: add squared deviations together and get mean. Random: A random outcome is when we know what could happen but it is uncertain as to what we'll get. IN the long run we can make predictions about the outcomes. Trial: doing one "run" of the experiment sequence Event: outcome of interest (can be a single outcome or a collection of outcomes)
Statistics-consists of techniques and tools that aid in the collection, organization, summarization, and interpretation of a collection of information referred to as data; it tells a story (p.1, Dawn Cooper)
Measurement- is a variable that takes on numeric values and could possibly use partial units. Ex. 5 1/2 years old, Ex: Height, weight
Real Graph- A graph constructed using real objects to display the data. (p.14, Chelsey Jones)
Categorical Variable- a variable that takes on categorical designations.Ex. eye color, major, age. (p. 13, Sarah Ross)
Catergorical Graphs: Real, Pictograph, bar graph, circle graph, stacked bar graph( Bailey)
Count- is a variable that takes on numerical variables and could not be answered in partial units. Ex. How many kids do you have? (Katie Minor)
Frequency: How much of the data, but frequency is not part of the data (Bailey)
Outcome: This helps you determine what variable you are dealing with (bailey)
READ: No calculations, Read the information An example " What % of the 5th grade sampled score in the profieciency level in science in 1997?" (Bailey)
DERIVE: Calculation, An example " How many of the 5th grade sample scored in the proficency level in science for 1999?" ( Bailey)
INTERRET: Predictions, organize the information/ Read beyond the data, summarizing the data, An example " Is there any pattern exhitbited in the 5th grade sample proficincy level science pattern from 1997-1999?" (Bailey)
DISTRIBUTION: How is the spread of the data (Bailey)
SPREAD OUT: Data distributed unevenly at 1 end to the outher (Bailey)
RANGE: Which tells us the difference between the lowest and highest points in our information, this tells us the SPREAD (Bailey)
SPREAD: This is a description of how data is distributed which = the range ( variability) (Bailey)
CLUMPS= clustering ( Bailey)
HOLES= gaps ( bailey)
BUMPS= mod ( Bailey)
DISTRIBUTION= spread ( Bailey)
SYMETRIC: the graph is equal on both sides, so if you were to cut the graph in half it would be the same on both sides (Bailey)
Measures of center: (Bailey)
- Mode: The outcome which occurs most often
- Median: The outcome value in the middle, when in order from least to greatest.. this is our balancing point..( Bailey)
- Mean: A fairshare of the outcomes, calculated by taking the sum of the outcomes divided by the number of outcomes (Jeannie) The mean can also be thought of as a balance point in a data set or as an "equalizer". (Dr B)
Outlier: a point on a box plot that is 1 1/2 box/interquartile range from the box. (KatieM)Deviation: how far values are from the mean.
Standard Deviation: the square root of the total sum of all the distances from the data points to the mean.
Mean Deviation: add absolute deviations together and get mean
Variance: add squared deviations together and get mean.
Random: A random outcome is when we know what could happen but it is uncertain as to what we'll get. IN the long run we can make predictions about the outcomes.
Trial: doing one "run" of the experiment sequence
Event: outcome of interest (can be a single outcome or a collection of outcomes)